Continuous stiffness optimization of mobile robot in automated fiber placement

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-07-16 DOI:10.1016/j.rcim.2024.102833

Lei Miao , Weidong Zhu , Yingjie Guo , Xiaokang Xu , Wei Liang , Zhijia Cai , Shubin Zhao , Yinglin Ke

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引用次数: 0

Abstract

The low stiffness of series robots limits their application in high-load precision manufacturing, such as automated fiber placement (AFP). This paper presents a stiffness optimization method to enhance the stiffness of plane-mobile robots in continuous fiber placement by simultaneously adjusting the robot's posture and the base position. A stiffness performance index suitable for evaluating the comprehensive stiffness of the robot during the AFP process is proposed, which is based on the fluctuation characteristics of the contact force in fiber placement. To maximize this index and the normal stiffness, the multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the two-objective optimization model under multiple constraints. The constrained area of the mobile robot base corresponding to a given path point is determined by the fixed-height slice of the robot's reachable point cloud. A novel method combining global discrete solution and local continuous solution (GD-LC) is proposed to solve the model efficiently, which reduces the search dimension of the MOPSO algorithm. Experimental results from fiber placement on an aircraft mold show that the proposed method can significantly improve the stiffness performance of the AFP robot, and the force-induced deformation after continuous stiffness optimization is reduced by 70.01 % on average. The optimized laying quality further validates the engineering value of the proposed method.

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移动机器人在自动纤维铺放中的连续刚度优化

系列机器人的低刚度限制了其在高负荷精密制造领域的应用，如自动纤维贴装（AFP）。本文提出了一种刚度优化方法，通过同时调整机器人的姿态和基座位置来增强平面移动机器人在连续纤维贴装过程中的刚度。根据纤维铺放过程中接触力的波动特性，提出了适用于评估 AFP 过程中机器人综合刚度的刚度性能指标。为了最大化该指标和法线刚度，采用了多目标粒子群优化算法（MOPSO）来求解多约束条件下的双目标优化模型。移动机器人基地与给定路径点对应的约束区域由机器人可达点云的定高切片确定。本文提出了一种结合全局离散解和局部连续解（GD-LC）的新方法来高效求解该模型，从而降低了 MOPSO 算法的搜索维度。在飞机模具上铺设纤维的实验结果表明，所提出的方法能显著改善 AFP 机器人的刚度性能，在连续刚度优化后，力引起的变形平均减少了 70.01%。优化后的铺设质量进一步验证了所提方法的工程价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.